Lesson 6. Spatial Queries

In spatial analysis, our goal is not just to make nice maps, but to actually run analyses that leverage the explicitly spatial nature of our data. The process of doing this is known as spatial analysis.

To construct spatial analyses, we string together series of spatial operations in such a way that the end result answers our question of interest. There are many such spatial operations. These are known as spatial queries.


Instructor Notes

We will start by reviewing the most fundamental set, which we’ll refer to as spatial queries. These can be divided into:

We’ll work through examples of each of those types of queries.

Then we’ll see an example of a very common spatial analysis that is a conceptual amalgam of those two types: proximity analysis.

library(sf)
library(tmap)

6.0 Load and prep some data

Let’s read in our census tracts data again.

census_tracts = st_read("notebook_data/census/Tracts/cb_2013_06_tract_500k.shp")
## Reading layer `cb_2013_06_tract_500k' from data source `/Users/pattyf/Documents/Dlab/workshops/2021/Geospatial-Fundamentals-in-R-with-sf/notebook_data/census/Tracts/cb_2013_06_tract_500k.shp' using driver `ESRI Shapefile'
## Simple feature collection with 8043 features and 9 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -124.4096 ymin: 32.53416 xmax: -114.1312 ymax: 42.00952
## Geodetic CRS:  NAD83
plot(census_tracts$geometry)

head(census_tracts)
## Simple feature collection with 6 features and 9 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -122.3049 ymin: 37.76456 xmax: -122.2074 ymax: 37.84851
## Geodetic CRS:  NAD83
##   STATEFP COUNTYFP TRACTCE             AFFGEOID       GEOID NAME LSAD   ALAND
## 1      06      001  400300 1400000US06001400300 06001400300 4003   CT 1105329
## 2      06      001  400900 1400000US06001400900 06001400900 4009   CT  420877
## 3      06      001  402200 1400000US06001402200 06001402200 4022   CT  712082
## 4      06      001  402800 1400000US06001402800 06001402800 4028   CT  398311
## 5      06      001  404800 1400000US06001404800 06001404800 4048   CT  628405
## 6      06      001  406100 1400000US06001406100 06001406100 4061   CT 1843685
##   AWATER                       geometry
## 1      0 MULTIPOLYGON (((-122.2642 3...
## 2      0 MULTIPOLYGON (((-122.2856 3...
## 3      0 MULTIPOLYGON (((-122.304 37...
## 4      0 MULTIPOLYGON (((-122.276 37...
## 5      0 MULTIPOLYGON (((-122.2182 3...
## 6  74875 MULTIPOLYGON (((-122.2387 3...

Then we’ll grab just the Alameda Country tracts.

census_tracts_ac = census_tracts[census_tracts$COUNTYFP=='001',]
plot(census_tracts_ac)

6.1 Measurement Queries

We’ll start off with some simple measurement queries.

For example, here’s how we can get the areas of each of our census tracts.

st_area(census_tracts_ac)[1:10]
## Units: [m^2]
##  [1] 1106564.4  435823.9  693541.5  400641.5  618815.4 1962114.5  370336.5
##  [8]  478238.0  587716.8  857795.3

Okay!

We got…

numbers!

…?

Question

  1. What do those numbers mean?
  2. What are the units?
  3. And if we’re not sure, how might be find out?

Let’s take a look at our CRS.

st_crs(census_tracts_ac)
## Coordinate Reference System:
##   User input: NAD83 
##   wkt:
## GEOGCRS["NAD83",
##     DATUM["North American Datum 1983",
##         ELLIPSOID["GRS 1980",6378137,298.257222101,
##             LENGTHUNIT["metre",1]]],
##     PRIMEM["Greenwich",0,
##         ANGLEUNIT["degree",0.0174532925199433]],
##     CS[ellipsoidal,2],
##         AXIS["latitude",north,
##             ORDER[1],
##             ANGLEUNIT["degree",0.0174532925199433]],
##         AXIS["longitude",east,
##             ORDER[2],
##             ANGLEUNIT["degree",0.0174532925199433]],
##     ID["EPSG",4269]]

Wow! We’re working in an unprojected CRS, with units of decimal degrees, but sf automatically gave us area measurements in square meters (rather than the rather irrational square degrees).

How did it do this? For unprojected CRS, sf calculates geodetic measurements (i.e. travel-distances across the earth’s curved surface). It uses the st_geod_area function for this; see docs for details.

That said, when doing spatial analysis, we will almost always want to work in a projected CRS that has natural distance units, such as meters!

Time to project!

(As previously, we’ll use UTM Zone 10N with a NAD83 data. This is a good choice for our region of interest.)

census_tracts_ac_utm10 = st_transform(census_tracts_ac, 26910)
st_crs(census_tracts_ac_utm10)
## Coordinate Reference System:
##   User input: EPSG:26910 
##   wkt:
## PROJCRS["NAD83 / UTM zone 10N",
##     BASEGEOGCRS["NAD83",
##         DATUM["North American Datum 1983",
##             ELLIPSOID["GRS 1980",6378137,298.257222101,
##                 LENGTHUNIT["metre",1]]],
##         PRIMEM["Greenwich",0,
##             ANGLEUNIT["degree",0.0174532925199433]],
##         ID["EPSG",4269]],
##     CONVERSION["UTM zone 10N",
##         METHOD["Transverse Mercator",
##             ID["EPSG",9807]],
##         PARAMETER["Latitude of natural origin",0,
##             ANGLEUNIT["degree",0.0174532925199433],
##             ID["EPSG",8801]],
##         PARAMETER["Longitude of natural origin",-123,
##             ANGLEUNIT["degree",0.0174532925199433],
##             ID["EPSG",8802]],
##         PARAMETER["Scale factor at natural origin",0.9996,
##             SCALEUNIT["unity",1],
##             ID["EPSG",8805]],
##         PARAMETER["False easting",500000,
##             LENGTHUNIT["metre",1],
##             ID["EPSG",8806]],
##         PARAMETER["False northing",0,
##             LENGTHUNIT["metre",1],
##             ID["EPSG",8807]]],
##     CS[Cartesian,2],
##         AXIS["(E)",east,
##             ORDER[1],
##             LENGTHUNIT["metre",1]],
##         AXIS["(N)",north,
##             ORDER[2],
##             LENGTHUNIT["metre",1]],
##     USAGE[
##         SCOPE["unknown"],
##         AREA["North America - 126°W to 120°W and NAD83 by country"],
##         BBOX[30.54,-126,81.8,-119.99]],
##     ID["EPSG",26910]]

Now let’s try our area calculation again.

st_area(census_tracts_ac_utm10)[1:10]
## Units: [m^2]
##  [1] 1105796.6  435518.4  693052.3  400361.5  618393.6 1960768.5  370083.9
##  [8]  477916.4  587326.6  857191.6

What if we compare areas calculated from our unprojected and projected CRS?

print(st_area(census_tracts_ac)[[1]])
## 1106564 [m^2]
print(st_area(census_tracts_ac_utm10)[[1]])
## 1105797 [m^2]

Hmmm… The numbers are a bit different…

You may have noticed that our census tracts already have an area column in them.

Let’s also compare those two results to this column.

print(st_area(census_tracts_ac)[[1]])
## 1106564 [m^2]
print(st_area(census_tracts_ac_utm10)[[1]])
## 1105797 [m^2]
print(census_tracts$ALAND[1])
## [1] 1105329

Question

What explains the discrepancy? Which areas are correct? Which are incorrect?

We can also sum the area for Alameda county by wrapping our area calculation in a call to sum.

sum(st_area(census_tracts_ac_utm10))
## 1948917581 [m^2]

We can actually look up how large Alameda County is to check our work.The county is 739 miles2, which is around 1,914,001,213 meters2. I’d say we’re pretty close!

Also, you may have been wondering how R is managing to tell us the units of our measurements.

It turns out that sf depends on the units package to track units.

This is super convenient! But there is a gotcha:

# convert to square kilometers
sum(st_area(census_tracts_ac_utm10)) / (1000^2)
## 1948.918 [m^2]

Oops! Our manual conversion to square kilometers gave us the right number but kept the now-wrong units!

Here’s the proper way to convert:

units::set_units(sum(st_area(census_tracts_ac_utm10)), km^2)
## 1948.918 [km^2]

Much nicer! In case you’re wondering how we knew the right abbreviation to use for kilometers, check out the leftmost column in this reference table:

# View(units::valid_udunits())

As it turns out, we can similarly use another attribute to get the features’ lengths.

NOTE: In this case, given we’re dealing with polygons, this is equivalent to getting the features’ perimeters.

st_length(census_tracts)[1:10]
## Units: [m]
##  [1] 5358.919 2757.904 5397.799 2682.912 3711.654 6409.748 2540.302 3090.729
##  [9] 4262.487 3936.085

6.2 Spatial Relationship Queries

Spatial relationship queries consider how two geometries or sets of geometries relate to one another in space. For example, you may want to know what schools are located within the City of Berkeley or what East Bay Regional Parks have land within Berkeley. You may also want to combine a measurement query with a spatial relationship query. Example, you may want to know the total length of freeways within the city of Berkeley.

Here is a list of some of the more commonly used sf spatial relationship operations.

These can be used to select features in one dataset based on their spatial relationship to another. In short, these can be used for spatial selections, or spatial subsetting in other words.


However, there are several other spatial relationship predicates, though some are more complex to properly employ. For example the following two operations only work with geometries that are completely aligned.

Enough talk. Let’s work through some examples.

Schools in Berkeley

Let’s load the CA Places data and select the city of Berkeley and save it to an sf dataframe.

places = st_read('notebook_data/census/Places/cb_2018_06_place_500k.shp')
## Reading layer `cb_2018_06_place_500k' from data source `/Users/pattyf/Documents/Dlab/workshops/2021/Geospatial-Fundamentals-in-R-with-sf/notebook_data/census/Places/cb_2018_06_place_500k.shp' using driver `ESRI Shapefile'
## Simple feature collection with 1521 features and 9 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -124.2694 ymin: 32.53416 xmax: -114.229 ymax: 41.99314
## Geodetic CRS:  NAD83
berkeley = places[places$NAME=='Berkeley',]
plot(berkeley$geometry)

Then let’s load the Alameda County schoolds data and select the schools within Berkeley.

First, load the CA

schools_df = read.csv('notebook_data/alco_schools.csv')
schools_sf = st_as_sf(schools_df, 
                      coords = c('X','Y'),
                      crs = 4326)

Check that the two sf dataframes have the same CRS.

st_crs(schools_sf) == st_crs(berkeley)
## [1] FALSE

They don’t have the same CRS so we need to align them. Let’s transform (or reproject) the CRS of both of these datasets to UTM10N, NAD83 (EPSG 26910). This is a commonly used CRS for Northern CA data.

# Transform data CRSs...
schools_utm10 <- st_transform(schools_sf, 26910)
berkeley_utm10 <- st_transform(berkeley, 26910)

If you look at the Schools data you will see that it has a City column. So we can subset the data by attribute to select the Schools in Berkeley. No need to do a spatial selection.

berkeley_schools = schools_utm10[schools_utm10$City=='Berkeley',]
dim(berkeley_schools)
## [1] 31  8
plot(berkeley_utm10$geometry)
plot(berkeley_schools$geometry, add=T)

But what if we didn’t have that city column? How could we identify the schools in Berkeley spatially?

berkeley_schools_spatial = schools_utm10[berkeley_utm10,]
dim(berkeley_schools_spatial)
## [1] 32  8
plot(berkeley_utm10$geometry)
plot(berkeley_schools_spatial$geometry, add=T)

Interstingly, we have one more school in Berkeley based on the spatial selection!? Let’s take a look

plot(berkeley_utm10$geometry)
plot(berkeley_schools_spatial$geometry, add=T)
plot(berkeley_schools$geometry,col="red", add=T)

tmap_mode('view')
## tmap mode set to interactive viewing
tm_shape(berkeley_utm10) +
  tm_borders() +
tm_shape(berkeley_schools_spatial) +
 tm_dots(col="black", size=.3) +
 tm_shape(berkeley_schools) +
 tm_dots(col="red", size=.1)

In a similar fashion, we can use the st_disjoint operator to select only those schools NOT spatial within Berkeley.

berkeley_schools_disjoint = schools_utm10[berkeley_utm10, , op=st_disjoint]
plot(berkeley_schools_disjoint$geometry)
plot(berkeley_utm10, col=NA, border="red", add=T)
## Warning in plot.sf(berkeley_utm10, col = NA, border = "red", add = T): ignoring
## all but the first attribute

Also, please keep in mind that there is no need to memorize these predicates and their functions! Here is a fantastic sf cheatsheet that lists and briefly explains all these common functions (and many more).

Protected Areas in Alameda County

Let’s load a new dataset to demonstrate these spatial queries in more detail.

This is a dataset containing all the protected areas (parks and the like) in California.

We will use this data and the Alameda County Census Data that we created earlier to ask “What protected areas are within Alameda County?”

First load the CPAD data (CA Protected Areas Database).

pas = st_read('./notebook_data/protected_areas/CPAD_2020a_Units.shp')
## Reading layer `CPAD_2020a_Units' from data source `/Users/pattyf/Documents/Dlab/workshops/2021/Geospatial-Fundamentals-in-R-with-sf/notebook_data/protected_areas/CPAD_2020a_Units.shp' using driver `ESRI Shapefile'
## Simple feature collection with 17068 features and 21 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -374984.2 ymin: -604454.8 xmax: 540016.3 ymax: 449743.2
## Projected CRS: NAD83 / California Albers

What is the CRS of the PAS data?

Let’s transform the data to match census_tracts_ac_utm10.

pas_utm10 = st_transform(pas, st_crs(census_tracts_ac_utm10))

Let’s plot the data in so that we know what to expect.

plot(census_tracts_ac_utm10$geometry, col='red')
plot(pas_utm10$geometry, col='green', add=T)

We can see from our map that some of the protected areas are completely within Alameda County and some of them overlap. To get both of these cases we use the st_intersects operator, which is the default. Let’s check it out.

pas_intersects = pas_utm10[census_tracts_ac_utm10,]  #st_intersects
pas_within = pas_utm10[census_tracts_ac_utm10, , op=st_within]
tmap_mode('view')
## tmap mode set to interactive viewing
tm_shape(census_tracts_ac_utm10)+
  tm_polygons(col="gray", border.col="grey") +
tm_shape(pas_intersects) +
  tm_borders(col="green") +
tm_shape(pas_within) +
  tm_borders(col='red')

What you can see from the above, is that by default, st_intersects returns the features that intersect but it does not clip the features to the boundary of Alameda County. For that, we would need to use a different spatial operation - st_intersection.

Let’s try it!

pas_in_ac = st_intersection(pas_utm10, census_tracts_ac_utm10)
## Warning: attribute variables are assumed to be spatially constant throughout all
## geometries

Great! Now, if we scroll the resulting sf object we’ll see that the COUNTY column of our resulting subset gives us a good sanity check on our results.

head(pas_in_ac)
## Simple feature collection with 6 features and 30 fields
## Geometry type: POLYGON
## Dimension:     XY
## Bounding box:  xmin: 561576 ymin: 4184117 xmax: 565367.9 ymax: 4188588
## Projected CRS: NAD83 / UTM zone 10N
##        ACCESS_TYP UNIT_ID                     UNIT_NAME SUID_NMA AGNCY_ID
## 8478  Open Access   13443                Hardy Dog Park    19677     1228
## 11167 Open Access   47996                  Redondo Park    32418     1228
## 4954  Open Access   14781           Temescal Creek Park    26230     1098
## 3585  Open Access   29165 Cypress Freeway Memorial Park    17873     1228
## 8131  Open Access   13451           South Prescott Park    25676     1228
## 9319  Open Access   15535               Mandela Parkway    21738     1228
##                AGNCY_NAME AGNCY_LEV   AGNCY_TYP
## 8478     Oakland, City of      City City Agency
## 11167    Oakland, City of      City City Agency
## 4954  Emeryville, City of      City City Agency
## 3585     Oakland, City of      City City Agency
## 8131     Oakland, City of      City City Agency
## 9319     Oakland, City of      City City Agency
##                                                   AGNCY_WEB LAYER MNG_AG_ID
## 8478  http://www2.oaklandnet.com/Government/o/opr/index.htm  City      1228
## 11167 http://www2.oaklandnet.com/Government/o/opr/index.htm  City      1228
## 4954          http://www.ci.emeryville.ca.us/158/City-Parks  City      1098
## 3585  http://www2.oaklandnet.com/Government/o/opr/index.htm  City      1228
## 8131  http://www2.oaklandnet.com/Government/o/opr/index.htm  City      1228
## 9319  http://www2.oaklandnet.com/Government/o/opr/index.htm  City      1228
##                MNG_AGENCY MNG_AG_LEV  MNG_AG_TYP PARK_URL  COUNTY  ACRES
## 8478     Oakland, City of       City City Agency     <NA> Alameda  0.788
## 11167    Oakland, City of       City City Agency     <NA> Alameda  0.250
## 4954  Emeryville, City of       City City Agency     <NA> Alameda  1.527
## 3585     Oakland, City of       City City Agency     <NA> Alameda  1.278
## 8131     Oakland, City of       City City Agency     <NA> Alameda  4.269
## 9319     Oakland, City of       City City Agency     <NA> Alameda 12.470
##                          LABEL_NAME YR_EST                DES_TP GAP_STS
## 8478                 Hardy Dog Park      0            Local Park       4
## 11167                  Redondo Park      0            Local Park       4
## 4954            Temescal Creek Park   1972            Local Park       4
## 3585  Cypress Freeway Memorial Park      0            Local Park       4
## 8131            South Prescott Park      0            Local Park       4
## 9319                Mandela Parkway      0 Local Recreation Area       4
##       STATEFP COUNTYFP TRACTCE             AFFGEOID       GEOID NAME LSAD
## 8478       06      001  400300 1400000US06001400300 06001400300 4003   CT
## 11167      06      001  400300 1400000US06001400300 06001400300 4003   CT
## 4954       06      001  400900 1400000US06001400900 06001400900 4009   CT
## 3585       06      001  402200 1400000US06001402200 06001402200 4022   CT
## 8131       06      001  402200 1400000US06001402200 06001402200 4022   CT
## 9319       06      001  402200 1400000US06001402200 06001402200 4022   CT
##         ALAND AWATER                       geometry
## 8478  1105329      0 POLYGON ((565274.7 4188544,...
## 11167 1105329      0 POLYGON ((565005.3 4188113,...
## 4954   420877      0 POLYGON ((563475.6 4188002,...
## 3585   712082      0 POLYGON ((562225.1 4184994,...
## 8131   712082      0 POLYGON ((561576 4184223, 5...
## 9319   712082      0 POLYGON ((562327.9 4184999,...

An overlay plot can also provide a nice check!

tm_shape(census_tracts_ac_utm10) + 
  tm_polygons(col='gray', border.col='gray') +
tm_shape(pas_in_ac) + 
  tm_polygons(col = 'ACRES', palette = 'YlGn',
              border.col = 'black', lwd = 0.4, 
              alpha = 0.8,
              title =  'Protected areas in Alameda County, colored by area')

st_intersects or st_intersection?

It really depends! But make sure you understand the difference.

Exercise: Spatial Relationship Query

Let’s use a spatial relationship query to create a new dataset containing BART stations in Berkeley.

Run the next two cells to load datasets containing Berkeley’s city boundary and Alameda County’s schools and to reproject them to match the Berkeley_utm10 dataframe (EPSG: 26910).

Then in the following cell, write your own code to: 1. Spatially select the BART stations that are within Berkeley 2. Plot the Berkeley boundary and then overlay the selected BART stations.

To see the solution, look at the hidden text below.

# load the Berkeley boundary
bart_df = st_read("notebook_data/transportation/bart.csv")
## Reading layer `bart' from data source `/Users/pattyf/Documents/Dlab/workshops/2021/Geospatial-Fundamentals-in-R-with-sf/notebook_data/transportation/bart.csv' using driver `CSV'
## Warning: no simple feature geometries present: returning a data.frame or tbl_df
bart_sf = st_as_sf(bart_df, 
                   coords = c('lon','lat'),
                   crs = 4326)
  
# transform to EPSG:26910
bart_utm10 = st_transform(bart_sf, st_crs(berkeley_utm10))

# display
head(berkeley_utm10)
## Simple feature collection with 1 feature and 9 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 559347.6 ymin: 4188961 xmax: 567371.4 ymax: 4195617
## Projected CRS: NAD83 / UTM zone 10N
##      STATEFP PLACEFP  PLACENS         AFFGEOID   GEOID     NAME LSAD    ALAND
## 1213      06   06000 02409837 1600000US0606000 0606000 Berkeley   25 27127391
##        AWATER                       geometry
## 1213 18715614 MULTIPOLYGON (((559347.6 41...

Plot the data together

plot(bart_utm10$geometry)
plot(berkeley_utm10$geometry, col='blue', add=T)

Now, in the cell below, write the code to spatial select the Berkeley BART stations, then make the map.

# YOUR CODE HERE:

Solution hidden here!

6.3 Proximity Analysis

Now that we’ve seen the basic idea of spatial measurement and relationship queries, let’s take a look at a common analysis that combines those concepts: promximity analysis.

Proximity analysis seeks to identify all features in a focal feature set that are within some maximum distance of features in a reference feature set.

A common workflow for this analysis is:

  1. Buffer (i.e. add a margin around) the reference dataset, out to the maximum distance.
  2. Run a spatial relationship query to find all focal features that intersect (or are within) the buffer.

Let’s read in our bike boulevard data again.

Then we’ll find out which of our Berkeley schools are within a block’s distance (200 meters) of the bike boulevards.

bike_blvds = st_read('notebook_data/transportation/BerkeleyBikeBlvds.geojson')
## Reading layer `BerkeleyBikeBlvds' from data source `/Users/pattyf/Documents/Dlab/workshops/2021/Geospatial-Fundamentals-in-R-with-sf/notebook_data/transportation/BerkeleyBikeBlvds.geojson' using driver `GeoJSON'
## Simple feature collection with 211 features and 10 fields
## Geometry type: MULTILINESTRING
## Dimension:     XY
## Bounding box:  xmin: 561541.2 ymin: 4189007 xmax: 566451.6 ymax: 4193483
## Projected CRS: WGS 84 / UTM zone 10N
plot(bike_blvds$geometry)

Of course, we need to reproject the boulevards to our projected CRS.

bike_blvds_utm10 = st_transform(bike_blvds, st_crs(berkeley_utm10))

Now we can create our 200 meter bike boulevard buffers.

bike_blvds_buf = st_buffer(bike_blvds_utm10, dist=200)

Now let’s overlay everything.

tm_shape(berkeley_utm10) + 
  tm_polygons(col = 'lightgrey') + 
tm_shape(bike_blvds_buf) + 
  tm_polygons(col = 'pink', alpha = 0.5) +
tm_shape(bike_blvds_utm10) + 
  tm_lines() + 
tm_shape(berkeley_schools_spatial) + 
  tm_dots(col = 'purple', size=0.2)

Great! Looks like we’re all ready to run our intersection to complete the proximity analysis. At this point (pun intended) it is a spatial selection (or intersection) operation, just like what we have done previously in this lesson.

schools_near_blvds = berkeley_schools_spatial[bike_blvds_buf,]
# or
#schools_near_blvds = st_intersection(schools_sf_utm10, bike_blvds_buf)

Now let’s overlay again, to see if the schools we subsetted make sense.

tm_shape(berkeley_utm10) + 
  tm_polygons(col = 'lightgrey') + 
tm_shape(bike_blvds_buf) + 
  tm_polygons(col = 'pink', alpha = 0.5) +
tm_shape(bike_blvds_utm10) + 
  tm_lines() + 
tm_shape(berkeley_schools_spatial) + 
  tm_dots(col = 'purple', size=0.2) +
tm_shape(schools_near_blvds) + 
  tm_dots(col = 'yellow', size=0.2)

Also note that if we want to find the pairwise distance matrix of the shortest distances between our schools and the bike boulevards, we can use the st_distance function.

st_distance(berkeley_schools_spatial, bike_blvds_utm10)

Similarly, we could use the function st_nearest_feature to get a vector of the row index for the nearest bike boulevard to each school.

st_nearest_feature(berkeley_schools_spatial, bike_blvds_utm10)
##  [1]  71 171  39 136  42  30 163 172 127 171 189 156 137  33 187 197  50 207 169
## [20] 102 125 137   3 109 207  76 135 173  56 102 146  76

Exercise: Proximity Analysis

Now it’s your turn to try out a proximity analysis!

Run the next cell to load our BART-system data, reproject it to EPSG: 26910, and subset it to Berkeley.

Then in the following cell, write your own code to find all schools within walking distance (1 km) of a BART station.

As a reminder, let’s break this into steps: 1. buffer your Berkeley BART stations to 1 km (HINT: remember your units!) 2. use the schools’ within attribute to check whether or not they’re within the buffers 3. subset the Berkeley schools using the object returned by your spatial relationship query

  1. as always, plot your results for a good visual check!

To see the solution, look at the hidden text below.

# load the BART stations from CSV
bart_stations = read.csv('notebook_data/transportation/bart.csv')
# coerce to an sf data.frame and set CRS to 4326
bart_stations_sf = st_as_sf(bart_stations, 
                             coords = c('lon', 'lat'),
                             crs = 4326)

# transform to utm zone 10 n (epsg:26910)
bart_stations_sf_utm10 = st_transform(bart_stations_sf, crs=26910)

# subset to berkeley
berkeley_bart = st_intersection(bart_stations_sf_utm10, berkeley_utm10)
## Warning: attribute variables are assumed to be spatially constant throughout all
## geometries
# YOUR CODE HERE:
# buffer the BART stations to 1 km

# spatially select the schools within the buffers

# Map with tmap
# plot the Berkeley boundary (for reference) in lightgrey

# add the BART stations (for reference) to the plot in green

# add the BART buffers (for check) in lightgreen

# add all Berkeley schools (for reference) in black

# add the schools near BART (for check) in yellow

Solution hidden here!

6.4 Recap

Leveraging what we’ve learned in our earlier lessons, we got to work with map overlays and start answering questions related to proximity. Key concepts include:

  • Measuring area and length
    • st_area,
    • st_length
    • st_distance
  • Relationship Queries
    • st_intersects,
    • st_intersection
    • st_within, etc.
  • Buffer analysis
    • st_buffer

 D-Lab @ University of California - Berkeley
 Team Geo